Abstract
The issue of adaptation of industrial automation system is considered in line with paradigm of multi-agent systems as a method of distributed artificial intelligence, wherein subsequent agents can be integrated into systems that jointly solve complex problems. In this regard we propose to use technique based on associative interrelation between the terms within multi-agents when piece of knowledge is represented as a function that maps a domain of clauses. While using trie-based structures (as possible representation technique) for good reasoning and decision making we can properly integrate metric information about the environment and semantic information provided by the user. In other words, we achieve another, higher level of representation of the environment when necessary knowledge is properly directed to actually reasoning (knowledge-based reasoning) and decision making.
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Acknowledgement
The authors would like to acknowledge Sh. Narzullaev, Master of Telecommunication Engineering specialty, Tashkent University of Information Technologies for providing support in getting software codes examples according to task statement.
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Yusupbekov, N.R., Gulyamov, S.M., Kasimov, S.S., Usmanova, N.B. (2019). Knowledge-Based Planning for Industrial Automation Systems: The Way to Support Decision Making. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_115
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DOI: https://doi.org/10.1007/978-3-030-04164-9_115
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